Publication

Article Metrics

Citations


Online attention

Combining state of the art opensource and proprietary machine learning technologies to build a data analysis pipeline for gasoline particulate filters[..]

DOI: 10.1595/205651322X16508983994949 DOI Help

Authors: Aakash Varambhia (Johnson Matthey Technology Centre) , Angela E. Goode (Johnson Matthey Technology Centre) , Ryutaro Sato (Johnson Matthey Technology Centre) , Trung Tran (Johnson Matthey Technology Centre) , Alissa Stratulat (ZEISS Microscopy) , Markus Boese (ZEISS Microscopy) , Gareth Hatton (Johnson Matthey Technology Centre) , Dogan Ozkaya (Johnson Matthey Technology Centre)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Johnson Matthey Technology Review

State: Published (Approved)
Published: April 2022

Open Access Open Access

Abstract: The performance of a particulate filter is determined by multi-scale properties that span the macro, meso and atomic scale. Traditionally, the primary role of a GPF is to reduce solid particles and liquid droplets. At the macroscale, transport of gas through a filter’s channels and interconnecting pores act as main transport arteries for catalytically active sites. At the mesoscale, the micropore structure is important for ensuring that there are enough active sites that are accessible for the gas to reach the catalyst nanoparticles. Whereas at the atomic scale, the structure of the catalyst material determines the performance and selectivity within the filter. Understanding all length scales requires a correlative approach but this is often quite difficult to achieve due to the number of software packages a scientist has to deal with. We demonstrate how current state of the art approaches in the field can be combined into a streamlined pipeline to characterise particulate filters by digitally reconstructing the sample, analysing it at high throughput, and eventually used as an input for gas flow simulations and better product design.

Journal Keywords: tomography; autocatalysts; machine learning; correlative imaging; GFP; particulate filter

Subject Areas: Information and Communication Technology, Materials, Technique Development

Diamond Offline Facilities: Electron Physical Sciences Imaging Centre (ePSIC)
Instruments: E01-JEM ARM 200CF

Added On: 05/05/2022 11:27

Documents:
varambhia_06a_ys.pdf

Discipline Tags:

Technique Development - Materials Science Artificial Intelligence Information & Communication Technologies Materials Science Data processing

Technical Tags:

Microscopy Electron Microscopy (EM) Transmission Electron Microscopy (TEM)